Computational protocol: Genetic Diversity in Passiflora Species Assessed by Morphological and ITS Sequence Analysis

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Protocol publication

[…] Data on morphological variables (leaf length, leaf width, stem width, tendril length, tendril width, bract length, bracts width, flower size, petal length, petal width, sepal length, sepal width, number of outer rows of corona, corona length, fruit weight, fruit length, fruit width, seed length, and seed width) were statistically analyzed using the SAS 9.0 for Windows. Single-factor analysis of variance (ANOVA) with post hoc Tukey's test (P ≤ 0.05) was used to compare the mean values. Discriminant analysis (DA) based on linear combinations of the predictor variables was used to find the maximum separation between the studied Passiflora species using XLSTAT 2013 for Windows.The electropherograms of the sequence fragments were inspected and assembled using Phred, Phrap, and Consed software in MacPro []. The chromatograms were analyzed with Phred, assembled with Phrap, and scanned with PolyPhred; the results were then viewed with the Consed program. Only good-quality fragments (sequence quality above 20) were chosen for each sample. The nine studied Passiflora accessions sequences were compiled and aligned using multiple sequence comparison by log-expectation (MUSCLE) []. Other 30 additional in-group sequences were obtained from the GenBank database (NCBI) and included in the alignment (). Sites where gaps were required to maintain the alignment of the sequences were treated as missing data. As in an earlier analysis, Mitostemma brevifilis and Paropsia madagascariensis were chosen as outgroups [].Phylogenetic relationships were developed using maximum likelihood (ML) and maximum parsimony (MP) with the MEGA 5.1 software []. Maximum likelihood is a method that seeks the tree that makes the data most likely. It applies an explicit criterion—the log—likelihood to compare the various models of nucleotide substitution in the presence of a large number of short sequences. Maximum likelihood tries to infer an evolutionary tree by finding the tree which maximizes the probability of observing the data []. The ML analysis using the Tamura 3-parameter model with gamma distributions (T92+G) was selected as the best-fitting substitution model. The best-fitting substitution method was chosen based on the Akaike information criterion (AIC), the Bayesian information criterion (BIC), and ln⁡L criterion. The model with the lowest AIC and BIC scores and the highest ln⁡L was chosen to best describe the substitution pattern []. Likelihood analysis was performed by initially determining the transition : transversion ratio (ts : tv) that maximized the log-likelihood value; more specifically, the range of ts : tv values was plotted against the corresponding inferred log-likelihoods. The sequences were analyzed using a heuristic search with bootstrap analysis based on 1000 replicates of the dataset. The resulting trees were saved and used as starting trees for random addition following nearest neighbor intersection (NNI) branch swapping. Maximum parsimony is based on the assumption that the most likely tree is the one that requires the fewest number of changes to explain the nucleotide sequence data in the alignment. Instead of models option used in other models, MEGA uses MP search model option to implement parsimony. The basic premise of parsimony is that taxa share a common characteristic because they inherited that characteristic from common ancestors []. We used the heuristic search method with simple taxon addition and tree bisection reconnection (TBR). The choice of nodes for branch swapping in the resulting parsimonious model was informed by bootstrap analyses consisting of 1000 replications of the heuristic search. The MEGA 5.1 program was also used to construct the phylogenetic tree and estimate sequence divergence []. […]

Pipeline specifications

Software tools PolyPhred, MUSCLE, MEGA
Applications Phylogenetics, Sanger sequencing, Nucleotide sequence alignment
Organisms Phyllostachys edulis